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04 · Training & Model Adaptation · Topic 04

What is Instruction Tuning?

Instruction tuning trains a model on examples of instructions and useful responses so it becomes better at following user requests.

What is it?

Instruction tuning is additional training that teaches a model how to respond to instructions.

It usually starts from an already pre-trained model. The training examples pair a request with a desired response, such as “Summarize this paragraph.” followed by a short useful summary, or “Translate this into French.” followed by a correct translation.

Think of it like...

Think of a knowledgeable employee learning how to follow workplace requests. Pre-training gives the employee broad knowledge and language; instruction tuning gives practice with “Summarize this,” “Classify that,” and “Answer in this format” until the employee follows requests more reliably.

How it works

1 · Pre-trained modelStart with broad abilityThe model already learned language and general patterns.
2 · ExamplesPair requests and answersEach example shows an instruction and a useful response.
3 · Additional trainingAdjust parametersTraining changes internal parameters to favor better responses.
4 · Learned patternsRecognize task shapesThe model gets better at formats, roles, and task-following behavior.
5 · Instruction-tuned modelFollow new requestsAt runtime, it applies those patterns to unfamiliar instructions.

Real-world examples

Summarization

Instruction: Summarize this article.

Response: A short summary.

Classification

Instruction: Classify this support ticket.

Response: Billing.

Structured response

Instruction: Return JSON with name and date.

Response: Structured output.

What it is NOT

Instruction Tuning ≠ Pre-training

Pre-training: Broad initial learning.

Instruction tuning: Additional training focused on following instructions.

Instruction Tuning ≠ Prompting

Instruction tuning: Changes model parameters.

Prompting: Changes the instructions supplied at runtime.

Instruction Tuning ≠ RAG

Instruction tuning: Changes model behavior through training.

RAG: Supplies external context during use.

Remember this

Instruction tuning uses training examples to make a model better at following instructions.

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